AIMC Topic: Infant, Extremely Premature

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Development of a simplified model and nomogram for the prediction of pulmonary hemorrhage in respiratory distress syndrome in extremely preterm infants.

BMC pediatrics Nov 21, 2024
BACKGROUND: Pulmonary hemorrhage (PH) in respiratory distress syndrome (RDS) in extremely preterm infants exhibits a high mortality rate and poor long-term outcomes. The aim of the present study was to develop a machine learning (ML) predictive model...
Humans Machine Learning Retrospective Studies Nomograms Lung Diseases Hemorrhage Respiratory Distress Syndrome, Newborn Female Infant, Newborn Male Infant, Extremely Premature
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Can deep learning classify cerebral ultrasound images for the detection of brain injury in very preterm infants?

European radiology Aug 30, 2024
OBJECTIVES: Cerebral ultrasound (CUS) is the main imaging screening tool in preterm infants. The aim of this work is to develop deep learning (DL) models that classify normal vs abnormal CUS to serve as a computer-aided detection tool providing timel...
Infant, Newborn Male Infant, Extremely Premature Deep Learning Humans Female Infant, Premature Nova Scotia Ultrasonography Brain Injuries Image Interpretation, Computer-Assisted
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Combining artificial intelligence and conventional statistics to predict bronchopulmonary dysplasia in very preterm infants using routinely collected clinical variables.

Pediatric pulmonology Aug 16, 2024
BACKGROUND: Prematurity is the strongest predictor of bronchopulmonary dysplasia (BPD). Most previous studies investigated additional risk factors by conventional statistics, while the few studies applying artificial intelligence, and specifically ma...
Gestational Age Bronchopulmonary Dysplasia Respiration, Artificial Machine Learning Infant, Premature Algorithms Female Infant, Newborn Risk Factors Male Infant, Extremely Premature Humans Artificial Intelligence
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A comprehensive study on machine learning models combining with oversampling for bronchopulmonary dysplasia-associated pulmonary hypertension in very preterm infants.

Respiratory research May 8, 2024
BACKGROUND: Bronchopulmonary dysplasia-associated pulmonary hypertension (BPD-PH) remains a devastating clinical complication seriously affecting the therapeutic outcome of preterm infants. Hence, early prevention and timely diagnosis prior to pathol...
Male Infant, Extremely Premature Bronchopulmonary Dysplasia Infant, Premature Retrospective Studies Female Infant, Newborn Hypertension, Pulmonary Humans Machine Learning
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Deep learning-based quantitative analyses of spontaneous movements and their association with early neurological development in preterm infants.

Scientific reports Feb 24, 2022
This study aimed to develop quantitative assessments of spontaneous movements in high-risk preterm infants based on a deep learning algorithm. Video images of spontaneous movements were recorded in very preterm infants at the term-equivalent age. The...
Male Infant Infant, Extremely Premature Deep Learning Movement Neural Networks, Computer Learning Female Infant, Newborn Humans
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Identifying clinical phenotypes in extremely low birth weight infants-an unsupervised machine learning approach.

European journal of pediatrics Nov 3, 2021
There is increasing evidence that patient heterogeneity significantly hinders advancement in clinical trials and individualized care. This study aimed to identify distinct phenotypes in extremely low birth weight infants. We performed an agglomerativ...
Gestational Age Humans Infant, Extremely Low Birth Weight Phenotype Infant, Newborn Infant, Extremely Premature Unsupervised Machine Learning Birth Weight
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Machine learning models for identifying preterm infants at risk of cerebral hemorrhage.

PloS one Jan 15, 2020
Intracerebral hemorrhage in preterm infants is a major cause of brain damage and cerebral palsy. The pathogenesis of cerebral hemorrhage is multifactorial. Among the risk factors are impaired cerebral autoregulation, infections, and coagulation disor...
Humans Machine Learning Models, Cardiovascular Risk Factors Male Retrospective Studies Infant, Extremely Premature Female Cerebral Hemorrhage Infant, Newborn
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Deep learning predicts extreme preterm birth from electronic health records.

Journal of biomedical informatics Oct 31, 2019
OBJECTIVE: Models for predicting preterm birth generally have focused on very preterm (28-32 weeks) and moderate to late preterm (32-37 weeks) settings. However, extreme preterm birth (EPB), before the 28th week of gestational age, accounts for the m...
Infant, Newborn Infant, Extremely Premature Deep Learning Datasets as Topic International Classification of Diseases Humans Algorithms Electronic Health Records
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Optimizing neural networks for medical data sets: A case study on neonatal apnea prediction.

Artificial intelligence in medicine Jul 25, 2019
OBJECTIVE: The neonatal period of a child is considered the most crucial phase of its physical development and future health. As per the World Health Organization, India has the highest number of pre-term births [1], with over 3.5 million babies born...
Apnea Gestational Age Intensive Care Units, Neonatal Clinical Decision Rules Algorithms Birth Weight Infant, Newborn Humans India Heart Rate Infant, Extremely Premature Neural Networks, Computer Datasets as Topic Support Vector Machine Deep Learning Infant, Premature Decision Trees
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Early prediction of cognitive deficits in very preterm infants using functional connectome data in an artificial neural network framework.

NeuroImage. Clinical Jan 31, 2018
Investigation of the brain's functional connectome can improve our understanding of how an individual brain's organizational changes influence cognitive function and could result in improved individual risk stratification. Brain connectome studies in...
Image Processing, Computer-Assisted Female Neuropsychological Tests Humans Oxygen Support Vector Machine Male Neural Networks, Computer Magnetic Resonance Imaging Cognition Disorders Infant Brain Gestational Age Infant, Extremely Premature Connectome
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